The Future of Big Data
A Gartner’s survey revealed that 48% of the companies invested in big data in 2016, and around three-quarters of the survey’s participants had already invested, or were looking forward to investing in data analytics. Big data assists companies in various sectors, from marketing to pharmaceutical companies to third sector organizations. In 2018, it was predicted that by 2020 the volume of data that is essential to analyze would astonishingly double.?
Forrester states that companies would try to sell their data. Therefore, it is certain that almost all companies worldwide, across numerous industries, offering a variety of products and services, are in the business of data. How long is big data analytics going to be a game-changer in the dynamically evolving marketplace? Let’s explore.?
Why Do Companies Need Big Data Analytics?
Data is useless without analytics. You can employ data that is available to you to increase profitability by its optimal analysis. The insights you obtain from analyzed data helps you take the most appropriate actions and take your business to the next level. Let’s look into the reasons why a company may need data analytics services.?
3 Big Data Analytics Trends for 2021 and Beyond
AI drives deeper insights and increasingly sophisticated automation
Artificial intelligence (AI) has been a game-changer for analytics. With the immense volume of structured and unstructured data that companies and their customers generate, even automated manuals forms of analytics can only scratch at the surface of what’s to be discovered.?
The easiest way to think of AI, as it is employed today, is machines – computers and software – that can learn for themselves. For instance, let’s explore a problem we might employ a computer to solve today. Which of our customers are the most valuable to us??
With traditional, non-learning computing at our disposal, perhaps we could take a stab by creating a database that shows us which customers spend the most money. However, what if a new customer appears who spends $100 in their first transaction with us? Would you classify them as more valuable than a customer who spent $10 a month for the past year? To comprehend that we require a lot more data, such as average customer’s lifetime value (LTV), and probably personal data about customer themselves such as their age, spending habits, or income level would also be helpful!?
It is a lot more complicated task to interpret, comprehend, and extract insights from all those datasets. AI assists here because it can interpret all of the data together and predict what potential lifetime value of a customer may be according to all that we know – whether or not we understand the connections ourselves. A key element of this is that it does not necessarily give you the "right" or "wrong" answers – it gives a range of probabilities and then refines its results according to how accurate those predictions turn out to be.?
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Rich new ways to explore and interpret data
The “final mile” of the entire analytics process – before we take action based on our discoveries – is data visualization. Traditionally, it is visualization that performs communication between machines and humans, taking the form of charts, graphs, and dashboards that highlight essential discoveries and assist us to determine what data suggests to be achieved.?
The issue here has been that not all people are good at identifying a potentially useful insight buried in a pile of statistics. As empowerment to act on data-driven insight becomes increasingly essential for everyone within an organization, new ways of communicating these discoveries are constantly evolving.?
The use of human language is one area where essential breakthroughs have been made. Analytics tools that allow us to ask questions of data and to get answers in clear, human language would greatly increase access to data and enhance overall data capabilities in the organization. This field of technology is called natural language processing (NLP).?
Another is new technologies that enable us to get a better visual overview and comprehension of data by fully immersing ourselves within it. Extended reality (XR) – a term that comprises virtual reality (VR) and augmented reality (AR) would clearly be seen to drive innovation here. VR is employed to create new types of visualizations that let us impart richer meaning from data, while AR can show us directly how the outcomes to data analytics impact the world in real-time. For instance, a mechanic trying to diagnose an issue with a car can perhaps look at the engine wearing AR glasses and get predictions on what components might have an issue and need to be replaced. In the near future, we should expect to see new ways of data visualization and communication that would widen access to analytics and insights.?
Hybrid cloud and the edge
Cloud computing is another notable trend that has had a significant impact on the way big data analytics is performed. The ability to access large data stores and act on real-time information without requiring expensive on-premises infrastructure has triggered the boom in apps and startups offering data-driven services on demand. However, complete reliance on public cloud providers is not the best model for every business, and when you entrust third parties for your entire data operations, there are inevitably concerns around security and governance.?
A lot of companies are now considering to shift to hybrid cloud systems, where some information is kept on Amazon Web Service, Microsoft Azure, or Google Cloud Servers, while other, maybe more personal or sensitive data, stays within proprietary walled garden. Cloud providers are increasingly on-board with this trend, providing "cloud-on-premises" solutions that potentially come with all the rich features and robustness of public cloud but let data owners have full custody of their data.?
Edge computing is another vigorous trend that would affect the impact of big data and analytics on our lives over the next year. Primarily it means devices that are built to process data where it is gathered, instead of sending it to the cloud for storage and analysis. Some data requires instant action and it’s risky to keep sending it backwards and forwards –a good instance is the data collected from sensors on autonomous vehicles. In other cases, consumers can be reassured that they have an extra level of privacy when insights can be obtained directly from their devices without them having to send data to any third party.
Conclusion
As we see and analyze the applications of big data analytics and the immense support it offers to companies, it is clear that it is here to stay. It is efficient and predicts most of the data right and saves time and cost. Therefore, for every area where big data analytics seeps the word “better” can be added in front of it i.e. better training, better security, better education, better business, and a lot more.?